The following diagram illustrates the flow of gate evaluation where, after the initial stabilization delay period and three sampling intervals, the deployment is approved. The release execution and deployment does not proceed if all gates do not succeed in the same interval and before the configured timeout. To account for such variations, all the gates are periodically re-evaluated until all of them are successful at the same time. Typically, gates are used in connection with incident management, problem management, change management, monitoring, and external approval systems.Īs most of the health parameters vary over time, regularly changing their status from healthy to unhealthy and back to healthy. Gates allow automatic collection of health signals from external services, and then promote the release when all the signals are successful at the same time or stop the deployment on timeout. In order to add validation steps in our deployment process, we can leverage the concept of Gates in Azure DevOps This was already part of the deployment of course and in this a key aspect of understanding if the deployment was healthy or not. This led me to a solution where the Azure DevOps pipelines would leverage the health-check used in the Traffic manager deployment. So I was looking for way to do operational validation in the least intrusive and re-usable way. Which resulted in downtime for VMchooser. That being said, one of the big learnings I had this week is that while having an automated landscape with Terraform, some changes are rather intrusive… Where I should have checked the output of the terraform plan stage, I failed to do so. Where I found that the serverless deployment added more value for me compared to a lower cost profile. As I can start off the same code base to basically run different deployment options in Azure. After having migrated VMchooser from a fully Serverless infrastructure to Containers, I am currently doing the opposite move.
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